用预测结果和边界不确定性，让医学影像特征的置信区间更精准高效。
ConRad: Efficient Conformal Prediction for Radiomics
- 基于图像、分割图和边界不确定性构建自适应置信区间。
- 在5个数据集171个特征上效率提升，覆盖率接近理论目标。
- 特别适合临床影像分析中需要可靠置信度的场景。
从医学影像和分割掩码中提取的放射组学特征常用于支持临床决策。实际中，这些特征通常由预测的分割掩码计算得出，但分割模型可能过度自信或校准不足，导致衍生测量值看似比实际更可靠。共形预测（CP）可提供无需分布假设的预测区间，并具有有限样本下的边际覆盖保证，但针对分割生成的放射组学，黑箱区间效率低下，因其忽略了测试时关于图像外观、掩码几何和分割不确定性的信息。本文提出 ConRad，一种针对标量放射组学目标的共形框架，利用来自预测掩码、输入图像、预测放射组学和边界不确定性的协变量构建自适应区间，同时保持覆盖性。在五个2D医学影像数据集和171个保留的放射组学目标上，我们证明 ConRad 相较基线在特征层面提升了效率，同时保持近名义的实证覆盖率。消融实验进一步表明，分割边界不确定性特征是区间效率提升的最大贡献者。
原文摘要 · Abstract (English)
Radiomic features derived from medical images and segmentation masks are used to support decision making in clinical imaging pipelines. In practice, these features are often computed from predicted masks, but segmentation models can be overconfident or poorly calibrated, making derived measurements appear more reliable than they are. Conformal prediction (CP) provides distribution-free prediction intervals with finite-sample marginal coverage guarantees, but black-box intervals for segmentation-derived radiomics can be inefficient because they ignore test-time information about image appearance, mask geometry, and segmentation uncertainty. We propose ConRad, a conformal framework for scalar radiomic targets that uses covariates derived from the predicted mask, input image, predicted radiomics, and boundary uncertainty to construct adaptive intervals while maintaining coverage. Across five 2D medical imaging datasets and 171 retained radiomic targets, we show that ConRad improves feature-level efficiency compared to baselines while maintaining near-nominal empirical coverage. Ablation results further indicate that segmentation boundary uncertainty features are the largest contributors to interval efficiency.
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